Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	28,411
2	Arizona	25,778
3	Florida	25,000
4	Mississippi	22,730
5	New York	21,900
6	New Jersey	21,040
7	Alabama	21,010
8	South Carolina	19,647
9	Georgia	19,051
10	Rhode Island	18,817
11	Nevada	18,512
12	District of Columbia	18,146
13	Texas	17,759
14	Tennessee	17,713
15	Massachusetts	17,601
16	Arkansas	16,577
17	Delaware	16,055
18	Maryland	16,014
19	Iowa	15,582
20	Illinois	15,572
21	Nebraska	14,834
22	California	14,533
23	Connecticut	14,183
24	Idaho	14,138
25	Utah	13,876
26	North Carolina	13,067
27	Virginia	11,803
28	Indiana	11,398
29	Wisconsin	11,237
30	Oklahoma	11,110
31	Kansas	11,016
32	South Dakota	10,922
33	Minnesota	10,915
34	New Mexico	10,703
35	North Dakota	10,126
36	Missouri	9,913
37	Michigan	9,760
38	Pennsylvania	9,707
39	Colorado	8,874
40	Ohio	8,703
41	Washington	8,696
42	Kentucky	8,223
43	Puerto Rico	7,145
44	Alaska	6,201
45	Wyoming	5,256
46	Oregon	5,094
47	New Hampshire	5,030
48	Montana	4,708
49	West Virginia	4,326
50	Maine	3,012
51	Hawaii	2,553
52	Vermont	2,342

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Alabama	320
2	Florida	293
3	Georgia	282
4	Nevada	258
5	Mississippi	247
6	Tennessee	245
7	Louisiana	230
8	Idaho	222
9	Puerto Rico	222
10	California	220
11	Arkansas	219
12	Texas	215
13	South Carolina	190
14	North Dakota	168
15	Oklahoma	145
16	Missouri	139
17	Minnesota	137
18	Indiana	136
19	Maryland	133
20	Iowa	131
21	Kansas	129
22	Illinois	127
23	North Carolina	126
24	Wisconsin	124
25	Hawaii	123
26	Kentucky	118
27	Arizona	115
28	Alaska	114
29	Virginia	111
30	South Dakota	110
31	District of Columbia	102
32	Nebraska	102
33	Utah	99
34	Washington	98
35	Ohio	87
36	Montana	81
37	New Mexico	76
38	Oregon	65
39	Delaware	64
40	Michigan	61
41	Rhode Island	61
42	Colorado	59
43	West Virginia	59
44	Pennsylvania	57
45	Massachusetts	49
46	New Jersey	36
47	New York	29
48	Wyoming	24
49	Connecticut	23
50	New Hampshire	14
51	Maine	8
52	Vermont	7

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,787
2	New York	1,663
3	Massachusetts	1,268
4	Connecticut	1,246
5	Rhode Island	958
6	Louisiana	922
7	District of Columbia	837
8	Michigan	653
9	Mississippi	642
10	Illinois	619
11	Delaware	606
12	Maryland	593
13	Pennsylvania	576
14	Arizona	571
15	Indiana	452
16	South Carolina	397
17	Georgia	389
18	Florida	385
19	Alabama	366
20	New Mexico	329
21	Colorado	324
22	Ohio	314
23	Nevada	312
24	Texas	312
25	New Hampshire	308
26	Minnesota	301
27	Iowa	296
28	Virginia	272
29	California	264
30	Washington	233
31	Missouri	227
32	North Carolina	209
33	Arkansas	183
34	Nebraska	183
35	Tennessee	179
36	Kentucky	177
37	Wisconsin	173
38	South Dakota	165
39	North Dakota	153
40	Oklahoma	152
41	Idaho	133
42	Kansas	132
43	Utah	107
44	Maine	92
45	Vermont	92
46	Puerto Rico	87
47	Oregon	85
48	West Virginia	78
49	Montana	70
50	Wyoming	48
51	Alaska	32
52	Hawaii	23

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Mississippi	7
2	Florida	5
3	Louisiana	5
4	South Carolina	5
5	Alabama	4
6	Nevada	4
7	Texas	4
8	Arizona	3
9	Arkansas	3
10	Georgia	3
11	California	2
12	Iowa	2
13	New Mexico	2
14	West Virginia	2
15	Delaware	1
16	Idaho	1
17	Indiana	1
18	Maryland	1
19	Massachusetts	1
20	Minnesota	1
21	Missouri	1
22	Montana	1
23	North Carolina	1
24	North Dakota	1
25	Puerto Rico	1
26	Tennessee	1
27	Washington	1
28	Alaska	0
29	Colorado	0
30	Connecticut	0
31	District of Columbia	0
32	Hawaii	0
33	Illinois	0
34	Kansas	0
35	Kentucky	0
36	Maine	0
37	Michigan	0
38	Nebraska	0
39	New Hampshire	0
40	New Jersey	0
41	New York	0
42	Ohio	0
43	Oklahoma	0
44	Oregon	0
45	Pennsylvania	0
46	Rhode Island	0
47	South Dakota	0
48	Utah	0
49	Vermont	0
50	Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	140,287	1	99
Lake	Tennessee	112,885	2	99
Lee	Arkansas	101,163	3	99
Dakota	Nebraska	96,275	4	99
Lincoln	Arkansas	93,904	5	99
Davidson	Tennessee	33,409	135	95
Richland	South Carolina	21,253	387	87
York	South Carolina	12,777	902	71
Orange	California	12,762	903	71
Pierce	Washington	6,973	1642	47

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,139	1	99
Randolph	Georgia	3,836	2	99
Galax city	Virginia	3,781	3	99
Terrell	Georgia	3,517	4	99
McKinley	New Mexico	3,167	5	99
Richland	South Carolina	368	689	78
Davidson	Tennessee	313	784	75
Orange	California	229	1028	67
Pierce	Washington	165	1274	59
York	South Carolina	103	1602	49

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.